Ensemble Deep Learning Approaches in Health Care:A Review  

作　　者：Aziz Alotaibi 

机构地区：[1]Department of Computer Science,College of Computing and Information Technology,Taif University,Taif,21974,Saudi Arabia

出　　处：《Computers, Materials & Continua》2025年第3期3741-3771,共31页计算机、材料和连续体(英文)

基　　金：funded by Taif University,Saudi Arabia,project No.(TU-DSPP-2024-263).

摘　　要：Deep learning algorithms have been rapidly incorporated into many different applications due to the increase in computational power and the availability of massive amounts of data.Recently,both deep learning and ensemble learning have been used to recognize underlying structures and patterns from high-level features to make predictions/decisions.With the growth in popularity of deep learning and ensemble learning algorithms,they have received significant attention from both scientists and the industrial community due to their superior ability to learn features from big data.Ensemble deep learning has exhibited significant performance in enhancing learning generalization through the use of multiple deep learning algorithms.Although ensemble deep learning has large quantities of training parameters,which results in time and space overheads,it performs much better than traditional ensemble learning.Ensemble deep learning has been successfully used in several areas,such as bioinformatics,finance,and health care.In this paper,we review and investigate recent ensemble deep learning algorithms and techniques in health care domains,medical imaging,health care data analytics,genomics,diagnosis,disease prevention,and drug discovery.We cover several widely used deep learning algorithms along with their architectures,including deep neural networks(DNNs),convolutional neural networks(CNNs),recurrent neural networks(RNNs),and generative adversarial networks(GANs).Common healthcare tasks,such as medical imaging,electronic health records,and genomics,are also demonstrated.Furthermore,in this review,the challenges inherent in reducing the burden on the healthcare system are discussed and explored.Finally,future directions and opportunities for enhancing healthcare model performance are discussed.

关 键 词：Deep learning ensemble learning deep ensemble learning deep learning approaches for health care health care 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]